A High-Payload Robotic Hopper Powered by Bidirectional Thrusters
Song Li, Songnan Bai, Ruihan Jia, Yixi Cai, Runze Ding, Yu Shi, Fu Zhang, Pakpong Chirarattananon
AI summary
Problem
Conventional aerial robots lack sufficient payload capacity, while legged robots often sacrifice agility and speed; existing hybrid platforms have not fully explored or validated high-payload hopping capabilities.
Approach
The team integrated bidirectional thrusters with a passive spring-loaded leg to enable both upward and downward thrust, and developed a refined dynamic model compressed via neural networks for efficient real-time control.
Key results
- Achieved a 9.1x payload-to-mass ratio (carrying up to 2 kg)
- Developed a refined stance-phase dynamic model for heavy payloads
- Implemented neural network compression for real-time onboard control
- Demonstrated obstacle leaping, sharp turns, and autonomous LiDAR mapping
Why it matters
This design bridges the gap between agile aerial platforms and heavy-lifting ground robots, enabling practical mobile sensing and mapping in complex, unstructured environments.
Abstract
Mobile robots have revolutionized various fields, offering solutions for manipulation, environmental monitoring, and exploration. However, payload capacity remains a limitation. This paper presents a novel thrust-based robotic hopper capable of carrying payloads up to 9 times its own weight while maintaining agile mobility over less structured terrain. The 220 gram robot carries up to 2 kg while hopping—–a capability that bridges the gap between high-payload ground robots and agile aerial platforms. Key advancements that enable this high-payload capacity include the integration of bidirectional thrusters, al- lowing for both upward and downward thrust generation to enhance energy management while hopping. Additionally, we present a refined model of dynamics that accounts for heavy payload conditions, particularly for large jumps. To address the increased computational demands, we employ a neural network compression technique, ensuring real-time onboard control. The robot’s capabilities are demonstrated through a series of exper- iments, including leaping over a high obstacle, executing sharp turns with large steps, as well as performing simple autonomous navigation while carrying a 730 g LiDAR payload. This showcases the robot’s potential for applications such as mobile sensing and mapping in challenging environments.